Overview

Dataset statistics

Number of variables21
Number of observations2864
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory470.0 KiB
Average record size in memory168.0 B

Variable types

Text1
Categorical3
Numeric17

Alerts

Adult_mortality is highly overall correlated with BMI and 13 other fieldsHigh correlation
Alcohol_consumption is highly overall correlated with Economy_status_Developed and 5 other fieldsHigh correlation
BMI is highly overall correlated with Adult_mortality and 7 other fieldsHigh correlation
Diphtheria is highly overall correlated with Adult_mortality and 8 other fieldsHigh correlation
Economy_status_Developed is highly overall correlated with Adult_mortality and 10 other fieldsHigh correlation
Economy_status_Developing is highly overall correlated with Adult_mortality and 10 other fieldsHigh correlation
GDP_per_capita is highly overall correlated with Adult_mortality and 13 other fieldsHigh correlation
Hepatitis_B is highly overall correlated with Diphtheria and 2 other fieldsHigh correlation
Incidents_HIV is highly overall correlated with Adult_mortality and 3 other fieldsHigh correlation
Infant_deaths is highly overall correlated with Adult_mortality and 14 other fieldsHigh correlation
Life_expectancy is highly overall correlated with Adult_mortality and 13 other fieldsHigh correlation
Measles is highly overall correlated with Adult_mortality and 8 other fieldsHigh correlation
Polio is highly overall correlated with Adult_mortality and 8 other fieldsHigh correlation
Region is highly overall correlated with Economy_status_Developed and 5 other fieldsHigh correlation
Schooling is highly overall correlated with Adult_mortality and 14 other fieldsHigh correlation
Thinness_five_nine_years is highly overall correlated with Adult_mortality and 10 other fieldsHigh correlation
Thinness_ten_nineteen_years is highly overall correlated with Adult_mortality and 10 other fieldsHigh correlation
Under_five_deaths is highly overall correlated with Adult_mortality and 15 other fieldsHigh correlation
Alcohol_consumption has 38 (1.3%) zerosZeros

Reproduction

Analysis started2024-02-07 13:53:35.049295
Analysis finished2024-02-07 13:54:17.028776
Duration41.98 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Distinct179
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:17.253380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length21
Mean length8.9162011
Min length4

Characters and Unicode

Total characters25536
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTurkiye
2nd rowSpain
3rd rowIndia
4th rowGuyana
5th rowIsrael
ValueCountFrequency (%)
and 80
 
2.1%
republic 80
 
2.1%
rep 80
 
2.1%
united 48
 
1.3%
the 48
 
1.3%
guinea 48
 
1.3%
arab 48
 
1.3%
st 32
 
0.9%
congo 32
 
0.9%
new 32
 
0.9%
Other values (202) 3232
86.0%
2024-02-07T19:54:17.755143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3792
 
14.8%
i 2224
 
8.7%
n 1904
 
7.5%
e 1856
 
7.3%
r 1392
 
5.5%
o 1264
 
4.9%
u 928
 
3.6%
896
 
3.5%
t 896
 
3.5%
l 864
 
3.4%
Other values (45) 9520
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 20544
80.5%
Uppercase Letter 3744
 
14.7%
Space Separator 896
 
3.5%
Other Punctuation 320
 
1.3%
Dash Punctuation 32
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3792
18.5%
i 2224
10.8%
n 1904
9.3%
e 1856
 
9.0%
r 1392
 
6.8%
o 1264
 
6.2%
u 928
 
4.5%
t 896
 
4.4%
l 864
 
4.2%
d 752
 
3.7%
Other values (16) 4672
22.7%
Uppercase Letter
ValueCountFrequency (%)
S 384
 
10.3%
B 320
 
8.5%
C 288
 
7.7%
M 272
 
7.3%
A 256
 
6.8%
R 256
 
6.8%
G 224
 
6.0%
T 224
 
6.0%
L 192
 
5.1%
I 176
 
4.7%
Other values (14) 1152
30.8%
Other Punctuation
ValueCountFrequency (%)
. 160
50.0%
, 144
45.0%
' 16
 
5.0%
Space Separator
ValueCountFrequency (%)
896
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24288
95.1%
Common 1248
 
4.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3792
15.6%
i 2224
 
9.2%
n 1904
 
7.8%
e 1856
 
7.6%
r 1392
 
5.7%
o 1264
 
5.2%
u 928
 
3.8%
t 896
 
3.7%
l 864
 
3.6%
d 752
 
3.1%
Other values (40) 8416
34.7%
Common
ValueCountFrequency (%)
896
71.8%
. 160
 
12.8%
, 144
 
11.5%
- 32
 
2.6%
' 16
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3792
 
14.8%
i 2224
 
8.7%
n 1904
 
7.5%
e 1856
 
7.3%
r 1392
 
5.5%
o 1264
 
4.9%
u 928
 
3.6%
896
 
3.5%
t 896
 
3.5%
l 864
 
3.4%
Other values (45) 9520
37.3%

Region
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
Africa
816 
European Union
432 
Asia
432 
Central America and Caribbean
304 
Rest of Europe
240 
Other values (4)
640 

Length

Max length29
Median length14
Mean length11.055866
Min length4

Characters and Unicode

Total characters31664
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMiddle East
2nd rowEuropean Union
3rd rowAsia
4th rowSouth America
5th rowMiddle East

Common Values

ValueCountFrequency (%)
Africa 816
28.5%
European Union 432
15.1%
Asia 432
15.1%
Central America and Caribbean 304
 
10.6%
Rest of Europe 240
 
8.4%
Middle East 224
 
7.8%
South America 192
 
6.7%
Oceania 176
 
6.1%
North America 48
 
1.7%

Length

2024-02-07T19:54:17.964347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-07T19:54:18.131280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
africa 816
15.8%
america 544
10.6%
european 432
 
8.4%
union 432
 
8.4%
asia 432
 
8.4%
central 304
 
5.9%
and 304
 
5.9%
caribbean 304
 
5.9%
rest 240
 
4.7%
of 240
 
4.7%
Other values (6) 1104
21.4%

Most occurring characters

ValueCountFrequency (%)
a 4016
12.7%
i 2928
 
9.2%
r 2688
 
8.5%
e 2464
 
7.8%
n 2384
 
7.5%
2288
 
7.2%
A 1792
 
5.7%
o 1584
 
5.0%
c 1536
 
4.9%
f 1056
 
3.3%
Other values (17) 8928
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24768
78.2%
Uppercase Letter 4608
 
14.6%
Space Separator 2288
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 4016
16.2%
i 2928
11.8%
r 2688
10.9%
e 2464
9.9%
n 2384
9.6%
o 1584
 
6.4%
c 1536
 
6.2%
f 1056
 
4.3%
t 1008
 
4.1%
s 896
 
3.6%
Other values (7) 4208
17.0%
Uppercase Letter
ValueCountFrequency (%)
A 1792
38.9%
E 896
19.4%
C 608
 
13.2%
U 432
 
9.4%
R 240
 
5.2%
M 224
 
4.9%
S 192
 
4.2%
O 176
 
3.8%
N 48
 
1.0%
Space Separator
ValueCountFrequency (%)
2288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 29376
92.8%
Common 2288
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 4016
13.7%
i 2928
 
10.0%
r 2688
 
9.2%
e 2464
 
8.4%
n 2384
 
8.1%
A 1792
 
6.1%
o 1584
 
5.4%
c 1536
 
5.2%
f 1056
 
3.6%
t 1008
 
3.4%
Other values (16) 7920
27.0%
Common
ValueCountFrequency (%)
2288
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31664
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 4016
12.7%
i 2928
 
9.2%
r 2688
 
8.5%
e 2464
 
7.8%
n 2384
 
7.5%
2288
 
7.2%
A 1792
 
5.7%
o 1584
 
5.0%
c 1536
 
4.9%
f 1056
 
3.3%
Other values (17) 8928
28.2%

Year
Real number (ℝ)

Distinct16
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.5
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:18.331355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2000
Q12003.75
median2007.5
Q32011.25
95-th percentile2015
Maximum2015
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.6105772
Coefficient of variation (CV)0.0022966761
Kurtosis-1.2094279
Mean2007.5
Median Absolute Deviation (MAD)4
Skewness0
Sum5749480
Variance21.257422
MonotonicityNot monotonic
2024-02-07T19:54:18.519354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2015 179
 
6.2%
2007 179
 
6.2%
2006 179
 
6.2%
2012 179
 
6.2%
2000 179
 
6.2%
2001 179
 
6.2%
2008 179
 
6.2%
2011 179
 
6.2%
2002 179
 
6.2%
2013 179
 
6.2%
Other values (6) 1074
37.5%
ValueCountFrequency (%)
2000 179
6.2%
2001 179
6.2%
2002 179
6.2%
2003 179
6.2%
2004 179
6.2%
2005 179
6.2%
2006 179
6.2%
2007 179
6.2%
2008 179
6.2%
2009 179
6.2%
ValueCountFrequency (%)
2015 179
6.2%
2014 179
6.2%
2013 179
6.2%
2012 179
6.2%
2011 179
6.2%
2010 179
6.2%
2009 179
6.2%
2008 179
6.2%
2007 179
6.2%
2006 179
6.2%

Infant_deaths
Real number (ℝ)

HIGH CORRELATION 

Distinct847
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.363792
Minimum1.8
Maximum138.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:18.728162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile3.1
Q18.1
median19.6
Q347.35
95-th percentile86.455
Maximum138.1
Range136.3
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation27.538117
Coefficient of variation (CV)0.90693931
Kurtosis0.39230049
Mean30.363792
Median Absolute Deviation (MAD)14.5
Skewness1.1041224
Sum86961.9
Variance758.34786
MonotonicityNot monotonic
2024-02-07T19:54:18.952485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 26
 
0.9%
3.5 23
 
0.8%
3.4 20
 
0.7%
4.3 20
 
0.7%
3.8 19
 
0.7%
3.1 19
 
0.7%
3.6 19
 
0.7%
2.3 18
 
0.6%
3.7 17
 
0.6%
3 17
 
0.6%
Other values (837) 2666
93.1%
ValueCountFrequency (%)
1.8 1
 
< 0.1%
1.9 2
 
0.1%
2 3
 
0.1%
2.1 5
 
0.2%
2.2 11
0.4%
2.3 18
0.6%
2.4 15
0.5%
2.5 14
0.5%
2.6 12
0.4%
2.7 8
0.3%
ValueCountFrequency (%)
138.1 1
< 0.1%
135.6 1
< 0.1%
132.9 1
< 0.1%
130.2 1
< 0.1%
127.9 1
< 0.1%
127.2 1
< 0.1%
124.1 1
< 0.1%
121.5 1
< 0.1%
120.9 1
< 0.1%
119.7 1
< 0.1%

Under_five_deaths
Real number (ℝ)

HIGH CORRELATION 

Distinct1035
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.938268
Minimum2.3
Maximum224.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:19.166489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile3.9
Q19.675
median23.1
Q366
95-th percentile139.3
Maximum224.9
Range222.6
Interquartile range (IQR)56.325

Descriptive statistics

Standard deviation44.569974
Coefficient of variation (CV)1.0380012
Kurtosis1.1826057
Mean42.938268
Median Absolute Deviation (MAD)17.2
Skewness1.3780539
Sum122975.2
Variance1986.4825
MonotonicityNot monotonic
2024-02-07T19:54:19.568595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1 29
 
1.0%
4 20
 
0.7%
4.9 18
 
0.6%
4.2 18
 
0.6%
3.9 18
 
0.6%
4.4 18
 
0.6%
2.8 17
 
0.6%
4.3 16
 
0.6%
5.2 16
 
0.6%
4.6 16
 
0.6%
Other values (1025) 2678
93.5%
ValueCountFrequency (%)
2.3 1
 
< 0.1%
2.4 2
 
0.1%
2.5 2
 
0.1%
2.6 3
 
0.1%
2.7 7
0.2%
2.8 17
0.6%
2.9 14
0.5%
3 9
0.3%
3.1 10
0.3%
3.2 11
0.4%
ValueCountFrequency (%)
224.9 2
0.1%
219.4 1
< 0.1%
215.2 1
< 0.1%
213.9 1
< 0.1%
208.1 1
< 0.1%
204.5 1
< 0.1%
204.4 1
< 0.1%
203.6 1
< 0.1%
202 1
< 0.1%
198.4 1
< 0.1%

Adult_mortality
Real number (ℝ)

HIGH CORRELATION 

Distinct2850
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.25178
Minimum49.384
Maximum719.3605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:19.761497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49.384
5-th percentile64.7474
Q1106.91025
median163.8415
Q3246.79138
95-th percentile429.11477
Maximum719.3605
Range669.9765
Interquartile range (IQR)139.88113

Descriptive statistics

Standard deviation114.91028
Coefficient of variation (CV)0.59770726
Kurtosis2.0303766
Mean192.25178
Median Absolute Deviation (MAD)65.01975
Skewness1.3776827
Sum550609.08
Variance13204.373
MonotonicityNot monotonic
2024-02-07T19:54:19.965414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.9455 6
 
0.2%
138.1465 3
 
0.1%
134.0135 2
 
0.1%
192.7545 2
 
0.1%
168.7145 2
 
0.1%
160.261 2
 
0.1%
78.1635 2
 
0.1%
94.6415 2
 
0.1%
215.093 2
 
0.1%
125.542 1
 
< 0.1%
Other values (2840) 2840
99.2%
ValueCountFrequency (%)
49.384 1
< 0.1%
49.998 1
< 0.1%
50.5745 1
< 0.1%
50.618 1
< 0.1%
50.9615 1
< 0.1%
51.6075 1
< 0.1%
51.6435 1
< 0.1%
51.776 1
< 0.1%
51.8135 1
< 0.1%
52.393 1
< 0.1%
ValueCountFrequency (%)
719.3605 1
< 0.1%
703.677 1
< 0.1%
703 1
< 0.1%
687.993 1
< 0.1%
686.639 1
< 0.1%
670.279 1
< 0.1%
653.918 1
< 0.1%
642.4155 1
< 0.1%
637.558 1
< 0.1%
629.815 1
< 0.1%

Alcohol_consumption
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1164
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8208816
Minimum0
Maximum17.87
Zeros38
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:20.169971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q11.2
median4.02
Q37.7775
95-th percentile11.9285
Maximum17.87
Range17.87
Interquartile range (IQR)6.5775

Descriptive statistics

Standard deviation3.9819486
Coefficient of variation (CV)0.82597935
Kurtosis-0.7324027
Mean4.8208816
Median Absolute Deviation (MAD)3.16
Skewness0.57119224
Sum13807.005
Variance15.855915
MonotonicityNot monotonic
2024-02-07T19:54:20.389982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
1.3%
0.19 18
 
0.6%
0.55 16
 
0.6%
0.01 15
 
0.5%
0.5 12
 
0.4%
0.012 12
 
0.4%
0.56 10
 
0.3%
0.46 10
 
0.3%
0.6 9
 
0.3%
1.03 9
 
0.3%
Other values (1154) 2715
94.8%
ValueCountFrequency (%)
0 38
1.3%
0.0001 1
 
< 0.1%
0.0003 1
 
< 0.1%
0.0004 1
 
< 0.1%
0.002 2
 
0.1%
0.003 5
 
0.2%
0.004 5
 
0.2%
0.005 2
 
0.1%
0.007 1
 
< 0.1%
0.01 15
 
0.5%
ValueCountFrequency (%)
17.87 1
< 0.1%
17.75 1
< 0.1%
17.47 1
< 0.1%
17.29 1
< 0.1%
16.96 1
< 0.1%
16.72 1
< 0.1%
16.61 1
< 0.1%
16.58 1
< 0.1%
16.27 1
< 0.1%
15.52 1
< 0.1%

Hepatitis_B
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.292598
Minimum12
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:20.611372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile48
Q178
median89
Q396
95-th percentile99
Maximum99
Range87
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.995511
Coefficient of variation (CV)0.18976175
Kurtosis2.7212574
Mean84.292598
Median Absolute Deviation (MAD)8
Skewness-1.6589733
Sum241414
Variance255.85638
MonotonicityNot monotonic
2024-02-07T19:54:20.814266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 239
 
8.3%
88 217
 
7.6%
98 214
 
7.5%
96 172
 
6.0%
95 162
 
5.7%
97 155
 
5.4%
94 128
 
4.5%
93 99
 
3.5%
92 94
 
3.3%
83 82
 
2.9%
Other values (70) 1302
45.5%
ValueCountFrequency (%)
12 1
 
< 0.1%
14 9
0.3%
17 1
 
< 0.1%
18 1
 
< 0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
22 2
 
0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
26 1
 
< 0.1%
ValueCountFrequency (%)
99 239
8.3%
98 214
7.5%
97 155
5.4%
96 172
6.0%
95 162
5.7%
94 128
4.5%
93 99
3.5%
92 94
 
3.3%
91 81
 
2.8%
90 66
 
2.3%

Measles
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.344972
Minimum10
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:21.016095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile38
Q164
median83
Q393
95-th percentile99
Maximum99
Range89
Interquartile range (IQR)29

Descriptive statistics

Standard deviation18.659693
Coefficient of variation (CV)0.24125282
Kurtosis0.71380913
Mean77.344972
Median Absolute Deviation (MAD)14
Skewness-0.99400827
Sum221516
Variance348.18413
MonotonicityNot monotonic
2024-02-07T19:54:21.228771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64 486
17.0%
83 227
 
7.9%
65 176
 
6.1%
99 155
 
5.4%
98 122
 
4.3%
97 119
 
4.2%
92 117
 
4.1%
95 117
 
4.1%
86 116
 
4.1%
96 112
 
3.9%
Other values (77) 1117
39.0%
ValueCountFrequency (%)
10 1
 
< 0.1%
12 1
 
< 0.1%
13 1
 
< 0.1%
14 3
 
0.1%
15 3
 
0.1%
16 13
0.5%
17 4
 
0.1%
18 2
 
0.1%
19 7
0.2%
21 4
 
0.1%
ValueCountFrequency (%)
99 155
5.4%
98 122
4.3%
97 119
4.2%
96 112
3.9%
95 117
4.1%
94 69
2.4%
93 64
2.2%
92 117
4.1%
91 56
 
2.0%
90 50
 
1.7%

BMI
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.032926
Minimum19.8
Maximum32.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:21.431099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19.8
5-th percentile21.2
Q123.2
median25.5
Q326.4
95-th percentile28.2
Maximum32.1
Range12.3
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation2.1939049
Coefficient of variation (CV)0.087640769
Kurtosis-0.15082512
Mean25.032926
Median Absolute Deviation (MAD)1.4
Skewness-0.12110644
Sum71694.3
Variance4.8132187
MonotonicityNot monotonic
2024-02-07T19:54:21.662720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 89
 
3.1%
26.1 86
 
3.0%
25.8 85
 
3.0%
26.2 84
 
2.9%
25.9 84
 
2.9%
25.7 78
 
2.7%
25.6 72
 
2.5%
26.3 70
 
2.4%
26.4 69
 
2.4%
25.5 67
 
2.3%
Other values (110) 2080
72.6%
ValueCountFrequency (%)
19.8 1
 
< 0.1%
19.9 3
 
0.1%
20 5
0.2%
20.1 5
0.2%
20.2 8
0.3%
20.3 8
0.3%
20.4 9
0.3%
20.5 8
0.3%
20.6 9
0.3%
20.7 12
0.4%
ValueCountFrequency (%)
32.1 3
0.1%
32 2
0.1%
31.9 2
0.1%
31.8 3
0.1%
31.7 3
0.1%
31.6 3
0.1%
31.5 2
0.1%
31.4 3
0.1%
31.3 2
0.1%
31.2 2
0.1%

Polio
Real number (ℝ)

HIGH CORRELATION 

Distinct77
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.499651
Minimum8
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:21.873591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile52
Q181
median93
Q397
95-th percentile99
Maximum99
Range91
Interquartile range (IQR)16

Descriptive statistics

Standard deviation15.080365
Coefficient of variation (CV)0.17434018
Kurtosis2.7269066
Mean86.499651
Median Absolute Deviation (MAD)5
Skewness-1.7264515
Sum247735
Variance227.41739
MonotonicityNot monotonic
2024-02-07T19:54:22.088185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 361
 
12.6%
98 249
 
8.7%
96 207
 
7.2%
97 200
 
7.0%
95 184
 
6.4%
94 153
 
5.3%
93 119
 
4.2%
92 104
 
3.6%
91 84
 
2.9%
88 73
 
2.5%
Other values (67) 1130
39.5%
ValueCountFrequency (%)
8 1
 
< 0.1%
14 1
 
< 0.1%
18 1
 
< 0.1%
21 2
0.1%
22 1
 
< 0.1%
23 1
 
< 0.1%
24 1
 
< 0.1%
26 2
0.1%
28 1
 
< 0.1%
30 4
0.1%
ValueCountFrequency (%)
99 361
12.6%
98 249
8.7%
97 200
7.0%
96 207
7.2%
95 184
6.4%
94 153
5.3%
93 119
 
4.2%
92 104
 
3.6%
91 84
 
2.9%
90 71
 
2.5%

Diphtheria
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.271648
Minimum16
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:22.298023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile50
Q181
median93
Q397
95-th percentile99
Maximum99
Range83
Interquartile range (IQR)16

Descriptive statistics

Standard deviation15.534225
Coefficient of variation (CV)0.18006176
Kurtosis3.1228577
Mean86.271648
Median Absolute Deviation (MAD)5
Skewness-1.8269276
Sum247082
Variance241.31214
MonotonicityNot monotonic
2024-02-07T19:54:22.504903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 343
 
12.0%
98 251
 
8.8%
97 201
 
7.0%
95 200
 
7.0%
96 196
 
6.8%
94 144
 
5.0%
93 113
 
3.9%
92 98
 
3.4%
91 95
 
3.3%
89 75
 
2.6%
Other values (70) 1148
40.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
19 1
 
< 0.1%
20 1
 
< 0.1%
21 1
 
< 0.1%
23 4
0.1%
24 3
0.1%
25 3
0.1%
26 3
0.1%
27 1
 
< 0.1%
28 2
0.1%
ValueCountFrequency (%)
99 343
12.0%
98 251
8.8%
97 201
7.0%
96 196
6.8%
95 200
7.0%
94 144
5.0%
93 113
 
3.9%
92 98
 
3.4%
91 95
 
3.3%
90 65
 
2.3%

Incidents_HIV
Real number (ℝ)

HIGH CORRELATION 

Distinct393
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89428771
Minimum0.01
Maximum21.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:22.712549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.08
median0.15
Q30.46
95-th percentile4.0185
Maximum21.68
Range21.67
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation2.3813895
Coefficient of variation (CV)2.6628896
Kurtosis28.688935
Mean0.89428771
Median Absolute Deviation (MAD)0.1
Skewness4.9787352
Sum2561.24
Variance5.6710158
MonotonicityNot monotonic
2024-02-07T19:54:22.915342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 318
 
11.1%
0.13 191
 
6.7%
0.17 177
 
6.2%
0.02 129
 
4.5%
0.03 111
 
3.9%
0.4 108
 
3.8%
0.05 103
 
3.6%
0.1 101
 
3.5%
0.04 90
 
3.1%
0.01 75
 
2.6%
Other values (383) 1461
51.0%
ValueCountFrequency (%)
0.01 75
 
2.6%
0.02 129
4.5%
0.03 111
 
3.9%
0.04 90
 
3.1%
0.05 103
 
3.6%
0.06 59
 
2.1%
0.07 38
 
1.3%
0.08 318
11.1%
0.09 51
 
1.8%
0.1 101
 
3.5%
ValueCountFrequency (%)
21.68 1
< 0.1%
20.8 1
< 0.1%
20.31 1
< 0.1%
20.22 1
< 0.1%
19.93 1
< 0.1%
19.87 1
< 0.1%
19.66 1
< 0.1%
18.94 1
< 0.1%
18.69 1
< 0.1%
18.67 1
< 0.1%

GDP_per_capita
Real number (ℝ)

HIGH CORRELATION 

Distinct2564
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11540.925
Minimum148
Maximum112418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:23.117174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum148
5-th percentile467
Q11415.75
median4217
Q312557
95-th percentile48361.6
Maximum112418
Range112270
Interquartile range (IQR)11141.25

Descriptive statistics

Standard deviation16934.789
Coefficient of variation (CV)1.4673684
Kurtosis6.4502315
Mean11540.925
Median Absolute Deviation (MAD)3408.5
Skewness2.3772484
Sum33053209
Variance2.8678708 × 108
MonotonicityNot monotonic
2024-02-07T19:54:23.326685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
554 5
 
0.2%
381 4
 
0.1%
468 4
 
0.1%
654 4
 
0.1%
4012 4
 
0.1%
501 4
 
0.1%
578 3
 
0.1%
3875 3
 
0.1%
3298 3
 
0.1%
655 3
 
0.1%
Other values (2554) 2827
98.7%
ValueCountFrequency (%)
148 1
< 0.1%
163 1
< 0.1%
174 1
< 0.1%
183 1
< 0.1%
192 1
< 0.1%
202 1
< 0.1%
213 1
< 0.1%
225 1
< 0.1%
237 1
< 0.1%
250 1
< 0.1%
ValueCountFrequency (%)
112418 1
< 0.1%
110095 1
< 0.1%
106544 1
< 0.1%
105614 1
< 0.1%
105584 1
< 0.1%
105462 1
< 0.1%
105339 1
< 0.1%
105290 1
< 0.1%
104574 1
< 0.1%
104488 1
< 0.1%

Population_mln
Real number (ℝ)

Distinct1803
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.675915
Minimum0.08
Maximum1379.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:23.526683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.18
Q12.0975
median7.85
Q323.6875
95-th percentile128.0425
Maximum1379.86
Range1379.78
Interquartile range (IQR)21.59

Descriptive statistics

Standard deviation136.48587
Coefficient of variation (CV)3.7214032
Kurtosis70.472298
Mean36.675915
Median Absolute Deviation (MAD)6.98
Skewness8.1576835
Sum105039.82
Variance18628.392
MonotonicityNot monotonic
2024-02-07T19:54:23.730697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11 39
 
1.4%
0.1 34
 
1.2%
0.09 23
 
0.8%
0.18 18
 
0.6%
0.28 16
 
0.6%
0.08 16
 
0.6%
0.75 14
 
0.5%
0.62 12
 
0.4%
2.06 11
 
0.4%
0.19 10
 
0.3%
Other values (1793) 2671
93.3%
ValueCountFrequency (%)
0.08 16
0.6%
0.09 23
0.8%
0.1 34
1.2%
0.11 39
1.4%
0.14 2
 
0.1%
0.15 3
 
0.1%
0.16 8
 
0.3%
0.17 8
 
0.3%
0.18 18
0.6%
0.19 10
 
0.3%
ValueCountFrequency (%)
1379.86 1
< 0.1%
1371.86 1
< 0.1%
1363.24 1
< 0.1%
1354.19 1
< 0.1%
1345.04 1
< 0.1%
1337.71 1
< 0.1%
1331.26 1
< 0.1%
1324.66 1
< 0.1%
1317.89 1
< 0.1%
1311.02 1
< 0.1%

Thinness_ten_nineteen_years
Real number (ℝ)

HIGH CORRELATION 

Distinct200
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.865852
Minimum0.1
Maximum27.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:23.930550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q11.6
median3.3
Q37.2
95-th percentile13.885
Maximum27.7
Range27.6
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation4.4382337
Coefficient of variation (CV)0.91211852
Kurtosis3.9089661
Mean4.865852
Median Absolute Deviation (MAD)2.3
Skewness1.7014871
Sum13935.8
Variance19.697918
MonotonicityNot monotonic
2024-02-07T19:54:24.131083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 74
 
2.6%
1.9 65
 
2.3%
0.8 64
 
2.2%
0.7 63
 
2.2%
1.2 62
 
2.2%
2.1 61
 
2.1%
2.2 58
 
2.0%
2 57
 
2.0%
0.9 57
 
2.0%
1.7 56
 
2.0%
Other values (190) 2247
78.5%
ValueCountFrequency (%)
0.1 23
 
0.8%
0.2 39
1.4%
0.3 32
1.1%
0.4 5
 
0.2%
0.5 35
1.2%
0.6 41
1.4%
0.7 63
2.2%
0.8 64
2.2%
0.9 57
2.0%
1 74
2.6%
ValueCountFrequency (%)
27.7 1
 
< 0.1%
27.5 1
 
< 0.1%
27.4 1
 
< 0.1%
27.3 1
 
< 0.1%
27.2 2
0.1%
27.1 2
0.1%
27 3
0.1%
26.9 2
0.1%
26.8 2
0.1%
26.7 1
 
< 0.1%

Thinness_five_nine_years
Real number (ℝ)

HIGH CORRELATION 

Distinct207
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8998254
Minimum0.1
Maximum28.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:24.350154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.6
median3.4
Q37.3
95-th percentile13.9
Maximum28.6
Range28.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.5252167
Coefficient of variation (CV)0.92354652
Kurtosis4.3025243
Mean4.8998254
Median Absolute Deviation (MAD)2.4
Skewness1.769183
Sum14033.1
Variance20.477586
MonotonicityNot monotonic
2024-02-07T19:54:24.559083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 69
 
2.4%
1.1 64
 
2.2%
0.5 63
 
2.2%
1.9 63
 
2.2%
2.1 61
 
2.1%
1.3 59
 
2.1%
1.5 57
 
2.0%
1.7 55
 
1.9%
0.6 54
 
1.9%
2 52
 
1.8%
Other values (197) 2267
79.2%
ValueCountFrequency (%)
0.1 31
1.1%
0.2 45
1.6%
0.3 25
 
0.9%
0.4 17
 
0.6%
0.5 63
2.2%
0.6 54
1.9%
0.7 46
1.6%
0.8 36
1.3%
0.9 69
2.4%
1 49
1.7%
ValueCountFrequency (%)
28.6 1
< 0.1%
28.5 1
< 0.1%
28.4 1
< 0.1%
28.3 1
< 0.1%
28.2 1
< 0.1%
28.1 1
< 0.1%
28 2
0.1%
27.9 1
< 0.1%
27.8 2
0.1%
27.7 1
< 0.1%

Schooling
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6321229
Minimum1.1
Maximum14.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:24.770937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.3
Q15.1
median7.8
Q310.3
95-th percentile12.3
Maximum14.1
Range13
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation3.1715556
Coefficient of variation (CV)0.41555353
Kurtosis-1.0392503
Mean7.6321229
Median Absolute Deviation (MAD)2.6
Skewness-0.15941024
Sum21858.4
Variance10.058765
MonotonicityNot monotonic
2024-02-07T19:54:24.980805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4 51
 
1.8%
9.8 45
 
1.6%
9.9 44
 
1.5%
10.9 42
 
1.5%
9.2 40
 
1.4%
8 38
 
1.3%
6.5 38
 
1.3%
10.5 37
 
1.3%
4.4 36
 
1.3%
9.1 36
 
1.3%
Other values (120) 2457
85.8%
ValueCountFrequency (%)
1.1 2
 
0.1%
1.2 5
 
0.2%
1.3 13
0.5%
1.4 17
0.6%
1.5 9
0.3%
1.6 12
0.4%
1.7 8
0.3%
1.8 6
 
0.2%
1.9 7
0.2%
2 8
0.3%
ValueCountFrequency (%)
14.1 1
 
< 0.1%
14 3
 
0.1%
13.9 1
 
< 0.1%
13.8 2
 
0.1%
13.7 2
 
0.1%
13.6 1
 
< 0.1%
13.4 4
0.1%
13.3 8
0.3%
13.2 6
0.2%
13.1 2
 
0.1%

Economy_status_Developed
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
0
2272 
1
592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2864
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2272
79.3%
1 592
 
20.7%

Length

2024-02-07T19:54:25.176375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-07T19:54:25.314468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2272
79.3%
1 592
 
20.7%

Most occurring characters

ValueCountFrequency (%)
0 2272
79.3%
1 592
 
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2864
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2272
79.3%
1 592
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2864
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2272
79.3%
1 592
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2272
79.3%
1 592
 
20.7%

Economy_status_Developing
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
1
2272 
0
592 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2864
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 2272
79.3%
0 592
 
20.7%

Length

2024-02-07T19:54:25.462261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-07T19:54:25.597329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2272
79.3%
0 592
 
20.7%

Most occurring characters

ValueCountFrequency (%)
1 2272
79.3%
0 592
 
20.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2864
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2272
79.3%
0 592
 
20.7%

Most occurring scripts

ValueCountFrequency (%)
Common 2864
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2272
79.3%
0 592
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2864
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2272
79.3%
0 592
 
20.7%

Life_expectancy
Real number (ℝ)

HIGH CORRELATION 

Distinct396
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.856075
Minimum39.4
Maximum83.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.5 KiB
2024-02-07T19:54:25.968158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39.4
5-th percentile50.6
Q162.7
median71.4
Q375.4
95-th percentile81
Maximum83.8
Range44.4
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation9.4056079
Coefficient of variation (CV)0.13659808
Kurtosis-0.21685113
Mean68.856075
Median Absolute Deviation (MAD)5.6
Skewness-0.77024161
Sum197203.8
Variance88.465461
MonotonicityNot monotonic
2024-02-07T19:54:26.176101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.6 27
 
0.9%
73.1 27
 
0.9%
74.1 25
 
0.9%
75 24
 
0.8%
73.6 23
 
0.8%
75.2 23
 
0.8%
74.9 23
 
0.8%
72.1 23
 
0.8%
72 22
 
0.8%
74.3 21
 
0.7%
Other values (386) 2626
91.7%
ValueCountFrequency (%)
39.4 1
 
< 0.1%
40.4 1
 
< 0.1%
41.4 1
 
< 0.1%
42.4 1
 
< 0.1%
42.5 1
 
< 0.1%
42.6 1
 
< 0.1%
42.7 3
0.1%
42.9 1
 
< 0.1%
43.1 2
0.1%
43.2 2
0.1%
ValueCountFrequency (%)
83.8 1
 
< 0.1%
83.6 1
 
< 0.1%
83.3 1
 
< 0.1%
83.2 2
 
0.1%
83.1 3
0.1%
82.9 4
0.1%
82.8 3
0.1%
82.7 5
0.2%
82.6 2
 
0.1%
82.5 5
0.2%

Interactions

2024-02-07T19:54:14.038847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:35.822865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:38.538384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:40.936811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:43.207159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:45.580866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:48.204267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:50.455342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:52.697983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:55.035643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:57.273712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:59.723664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:01.977896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:04.350096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:06.768756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:09.309771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:11.684124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:14.183390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:35.988928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:38.689435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:41.085701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:43.359887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:45.734348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:48.353173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:50.597473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:52.843512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:55.177516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:57.419822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:59.870624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:02.126955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:04.508987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:06.917777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:09.459470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:11.830529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:14.330357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:36.178883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:38.835016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:41.225444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:43.505991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:45.887381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:48.488184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:50.737291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:52.988603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:55.316579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:57.559509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:00.007635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:02.274148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:04.656227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:07.055767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:09.603394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:11.984329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:14.462653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:36.334793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:38.970861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:41.353080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:43.639743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:46.024530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:48.616179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:50.862283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:53.121511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:55.441295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:57.684471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:00.130685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:02.404113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:04.793138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:07.190528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:09.735680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:12.114346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:14.602726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:36.484860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:39.116047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:41.490141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:43.779655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:46.171006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:48.752428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:50.997225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:53.261957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:55.582370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:57.823519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:00.273043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:02.545683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:04.937326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:07.335128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:09.878612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:12.260686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:14.747795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:36.637538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:39.269869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:41.635547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:43.932318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:46.321144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:48.895455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:51.141643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:53.412058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:55.723426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:58.164404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:00.418230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:02.693410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:05.093386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:07.481043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:10.028545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:12.407609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:14.871655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:36.773351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:39.400435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:41.762781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:44.064604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:46.455661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:49.018375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:51.265774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:53.539469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:55.844895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:58.292478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:00.546268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:02.824466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:05.228902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:07.613757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:10.159962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:12.540506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:14.997743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:36.905690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:39.533842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:41.887760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:44.199351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:46.590611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:49.142325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:51.388765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:53.671413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:55.968951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:58.413807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:00.670272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:02.954652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:05.366217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:07.744488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:10.295625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:12.669465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:15.134830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:37.083584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:39.676831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:42.023375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:44.337612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:46.734644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:49.276983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:51.523632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:53.808481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:56.100502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:58.546501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:00.806190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:03.096592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:05.510310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:07.883579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:10.439875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:12.812588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:15.260370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:37.218269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:39.809843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:42.147124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:44.472613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:46.868124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:49.399001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:51.645380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:53.939257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:56.224402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:58.671448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:00.927990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:03.228429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:05.641593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:08.013558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:10.571953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:12.940924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:15.390549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:37.544497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:39.943152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:42.273053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:44.605666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:47.006961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:49.527846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:51.769409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:54.067327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:56.346245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:58.795393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:01.051184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:03.344990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:05.779605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:08.143759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:10.705186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:13.072036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:15.513620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:37.680190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:40.076141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:42.398059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:44.735502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:47.141595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:49.650088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:51.894760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:54.196372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:56.471062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:58.919341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:01.176152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:03.497608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:05.911622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:08.273594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:10.833923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:13.200942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:15.647883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:37.820221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:40.226328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:42.530545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:44.874395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:47.480735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:49.781983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:52.026704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:54.330831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:56.601019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:59.050500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:01.308284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:03.630893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:06.053604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:08.409349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:10.974967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:13.339145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:15.793376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:37.969221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:40.380748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:42.673882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:45.020165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:47.631345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:49.923964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:52.166591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:54.482716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:56.742794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:59.194300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:01.451910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:03.785167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:06.199327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:08.555986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:11.124073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:13.487400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:15.935729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:38.112217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:40.520713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:42.806776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:45.160732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:47.774865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:50.053503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:52.296579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:54.620652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:56.872767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:59.324289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:01.582743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:03.927303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:06.344479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:08.693931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:11.262164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:13.623686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:16.078753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:38.259681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:40.665287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:42.945095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:45.304718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:47.921915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:50.191142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:52.436146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:54.764504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:57.006912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:59.463331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:01.716762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:04.073494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:06.489536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:08.831906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:11.404084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:13.770161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:16.214663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:38.404601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:40.807234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:43.080163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:45.448662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:48.068627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:50.326537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:52.574092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:54.903413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:57.141755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:53:59.598349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:01.853906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:04.215396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:06.630632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:09.177793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:11.549037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-07T19:54:13.907290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-07T19:54:26.329884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Adult_mortalityAlcohol_consumptionBMIDiphtheriaEconomy_status_DevelopedEconomy_status_DevelopingGDP_per_capitaHepatitis_BIncidents_HIVInfant_deathsLife_expectancyMeaslesPolioPopulation_mlnRegionSchoolingThinness_five_nine_yearsThinness_ten_nineteen_yearsUnder_five_deathsYear
Adult_mortality1.000-0.283-0.555-0.5270.5640.564-0.784-0.3750.6540.862-0.959-0.509-0.5290.0280.322-0.6480.5470.5380.871-0.143
Alcohol_consumption-0.2831.0000.2990.3260.7170.7170.5500.180-0.109-0.5620.4480.3000.302-0.0800.3520.622-0.478-0.490-0.5560.006
BMI-0.5550.2991.0000.3850.4180.4180.6490.380-0.304-0.6080.5850.4400.392-0.2530.3670.623-0.571-0.559-0.6140.188
Diphtheria-0.5270.3260.3851.0000.3510.3510.5270.807-0.375-0.6270.5760.6110.948-0.0910.1920.512-0.252-0.244-0.6240.129
Economy_status_Developed0.5640.7170.4180.3511.0000.9990.6220.096-0.453-0.6700.6120.2970.2810.0610.8690.618-0.564-0.563-0.6690.000
Economy_status_Developing0.5640.7170.4180.3510.9991.000-0.622-0.0960.4530.670-0.612-0.297-0.281-0.0610.869-0.6180.5640.5630.6690.000
GDP_per_capita-0.7840.5500.6490.5270.622-0.6221.0000.375-0.428-0.8720.8460.5360.513-0.1440.2890.771-0.569-0.565-0.8740.084
Hepatitis_B-0.3750.1800.3800.8070.096-0.0960.3751.000-0.258-0.4550.4110.5540.786-0.1800.1840.358-0.123-0.110-0.4520.188
Incidents_HIV0.654-0.109-0.304-0.375-0.4530.453-0.428-0.2581.0000.567-0.614-0.431-0.377-0.0700.180-0.4890.3390.3630.581-0.036
Infant_deaths0.862-0.562-0.608-0.627-0.6700.670-0.872-0.4550.5671.000-0.934-0.606-0.6270.0830.318-0.8300.6150.6130.998-0.152
Life_expectancy-0.9590.4480.5850.5760.612-0.6120.8460.411-0.614-0.9341.0000.5430.570-0.0200.3570.739-0.599-0.594-0.9400.164
Measles-0.5090.3000.4400.6110.297-0.2970.5360.554-0.431-0.6060.5431.0000.631-0.1760.2660.551-0.305-0.281-0.6120.079
Polio-0.5290.3020.3920.9480.281-0.2810.5130.786-0.377-0.6270.5700.6311.000-0.1050.1830.518-0.259-0.251-0.6250.114
Population_mln0.028-0.080-0.253-0.0910.061-0.061-0.144-0.180-0.0700.083-0.020-0.176-0.1051.0000.294-0.0670.0920.0760.0890.040
Region0.3220.3520.3670.1920.8690.8690.2890.1840.1800.3180.3570.2660.1830.2941.0000.564-0.598-0.594-0.6200.000
Schooling-0.6480.6220.6230.5120.618-0.6180.7710.358-0.489-0.8300.7390.5510.518-0.0670.5641.000-0.602-0.615-0.8340.149
Thinness_five_nine_years0.547-0.478-0.571-0.252-0.5640.564-0.569-0.1230.3390.615-0.599-0.305-0.2590.092-0.598-0.6021.0000.9470.619-0.035
Thinness_ten_nineteen_years0.538-0.490-0.559-0.244-0.5630.563-0.565-0.1100.3630.613-0.594-0.281-0.2510.076-0.594-0.6150.9471.0000.617-0.037
Under_five_deaths0.871-0.556-0.614-0.624-0.6690.669-0.874-0.4520.5810.998-0.940-0.612-0.6250.089-0.620-0.8340.6190.6171.000-0.153
Year-0.1430.0060.1880.1290.0000.0000.0840.188-0.036-0.1520.1640.0790.1140.0400.0000.149-0.035-0.037-0.1531.000

Missing values

2024-02-07T19:54:16.422786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-07T19:54:16.850572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryRegionYearInfant_deathsUnder_five_deathsAdult_mortalityAlcohol_consumptionHepatitis_BMeaslesBMIPolioDiphtheriaIncidents_HIVGDP_per_capitaPopulation_mlnThinness_ten_nineteen_yearsThinness_five_nine_yearsSchoolingEconomy_status_DevelopedEconomy_status_DevelopingLife_expectancy
0TurkiyeMiddle East201511.113.0105.82401.32976527.897970.081100678.534.94.87.80176.5
1SpainEuropean Union20152.73.357.902510.35979426.097970.092574246.440.60.59.71082.8
2IndiaAsia200751.567.9201.07651.57603521.267640.1310761183.2127.128.05.00165.4
3GuyanaSouth America200632.840.5222.19655.68937425.392930.7941460.755.75.57.90167.0
4IsraelMiddle East20123.44.357.95102.89978927.094940.08339957.911.21.112.81081.7
5Costa RicaCentral America and Caribbean20069.811.295.22004.19888626.489890.1691104.352.01.97.90178.2
6Russian FederationRest of Europe20156.68.2223.00008.06979726.297970.089313144.102.32.312.00171.2
7HungaryEuropean Union20008.710.1192.969012.23889925.999990.08897110.212.32.310.21071.2
8JordanMiddle East200122.026.1129.76400.52978727.997990.1337085.224.03.99.60171.9
9MoldovaRest of Europe200815.317.8217.85707.72979226.596900.4322352.872.93.110.90168.7
CountryRegionYearInfant_deathsUnder_five_deathsAdult_mortalityAlcohol_consumptionHepatitis_BMeaslesBMIPolioDiphtheriaIncidents_HIVGDP_per_capitaPopulation_mlnThinness_ten_nineteen_yearsThinness_five_nine_yearsSchoolingEconomy_status_DevelopedEconomy_status_DevelopingLife_expectancy
2854FijiOceania201320.123.7189.54202.530999427.499990.1149020.874.03.710.30167.0
2855FinlandEuropean Union20013.44.298.83658.940889225.895980.08387855.190.90.89.41078.0
2856JapanAsia20142.12.855.58208.400839422.799960.1734387127.282.01.712.51083.6
2857BelarusRest of Europe20009.912.7239.716012.920709225.799990.0526179.982.72.88.90168.9
2858Iran, Islamic Rep.Middle East200719.723.1129.93350.023976525.698990.14530571.347.57.88.90172.6
2859NigerAfrica200097.0224.9291.82400.092726420.841340.4939911.3312.812.91.10149.9
2860MongoliaAsia200923.928.6235.23306.560979725.396950.0225152.672.22.39.10166.9
2861Sri LankaAsia200417.728.9134.89501.560629521.997970.02213019.3915.415.510.30174.3
2862LithuaniaEuropean Union20027.99.9204.012011.000949526.197950.0574243.443.33.311.11071.8
2863IcelandRest of Europe20112.12.650.57456.840889026.195950.05489340.320.90.911.01082.4